mirror of
https://github.com/pjreddie/darknet.git
synced 2023-08-10 21:13:14 +03:00
cleaned up data parsing a lot. probably nothing broken?
This commit is contained in:
Joseph Redmon
parent
7c120aef23
commit
e36182cd8c
753
src/cnn.c
753
src/cnn.c
@ -18,18 +18,18 @@
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void test_convolve()
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{
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image dog = load_image("dog.jpg",300,400);
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printf("dog channels %d\n", dog.c);
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image kernel = make_random_image(3,3,dog.c);
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image edge = make_image(dog.h, dog.w, 1);
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int i;
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clock_t start = clock(), end;
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for(i = 0; i < 1000; ++i){
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convolve(dog, kernel, 1, 0, edge, 1);
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}
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end = clock();
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printf("Convolutions: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
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show_image_layers(edge, "Test Convolve");
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image dog = load_image("dog.jpg",300,400);
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printf("dog channels %d\n", dog.c);
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image kernel = make_random_image(3,3,dog.c);
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image edge = make_image(dog.h, dog.w, 1);
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int i;
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clock_t start = clock(), end;
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for(i = 0; i < 1000; ++i){
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convolve(dog, kernel, 1, 0, edge, 1);
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}
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end = clock();
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printf("Convolutions: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
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show_image_layers(edge, "Test Convolve");
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}
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#ifdef GPU
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@ -37,11 +37,11 @@ void test_convolve()
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void test_convolutional_layer()
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{
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int i;
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image dog = load_image("data/dog.jpg",224,224);
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network net = parse_network_cfg("cfg/convolutional.cfg");
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// data test = load_cifar10_data("data/cifar10/test_batch.bin");
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// float *X = calloc(net.batch*test.X.cols, sizeof(float));
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// float *y = calloc(net.batch*test.y.cols, sizeof(float));
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image dog = load_image("data/dog.jpg",224,224);
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network net = parse_network_cfg("cfg/convolutional.cfg");
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// data test = load_cifar10_data("data/cifar10/test_batch.bin");
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// float *X = calloc(net.batch*test.X.cols, sizeof(float));
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// float *y = calloc(net.batch*test.y.cols, sizeof(float));
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int in_size = get_network_input_size(net)*net.batch;
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int del_size = get_network_output_size_layer(net, 0)*net.batch;
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int size = get_network_output_size(net)*net.batch;
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@ -50,7 +50,7 @@ void test_convolutional_layer()
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for(i = 0; i < in_size; ++i){
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X[i] = dog.data[i%get_network_input_size(net)];
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}
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// get_batch(test, net.batch, X, y);
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// get_batch(test, net.batch, X, y);
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clock_t start, end;
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cl_mem input_cl = cl_make_array(X, in_size);
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cl_mem truth_cl = cl_make_array(y, size);
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@ -73,41 +73,41 @@ void test_convolutional_layer()
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float *gpu_del = calloc(del_size, sizeof(float));
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memcpy(gpu_del, get_network_delta_layer(net, 0), del_size*sizeof(float));
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/*
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start = clock();
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forward_network(net, X, y, 1);
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backward_network(net, X);
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float cpu_cost = get_network_cost(net);
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end = clock();
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float cpu_sec = (float)(end-start)/CLOCKS_PER_SEC;
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float *cpu_out = calloc(size, sizeof(float));
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memcpy(cpu_out, get_network_output(net), size*sizeof(float));
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float *cpu_del = calloc(del_size, sizeof(float));
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memcpy(cpu_del, get_network_delta_layer(net, 0), del_size*sizeof(float));
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/*
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start = clock();
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forward_network(net, X, y, 1);
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backward_network(net, X);
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float cpu_cost = get_network_cost(net);
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end = clock();
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float cpu_sec = (float)(end-start)/CLOCKS_PER_SEC;
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float *cpu_out = calloc(size, sizeof(float));
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memcpy(cpu_out, get_network_output(net), size*sizeof(float));
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float *cpu_del = calloc(del_size, sizeof(float));
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memcpy(cpu_del, get_network_delta_layer(net, 0), del_size*sizeof(float));
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float sum = 0;
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float del_sum = 0;
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for(i = 0; i < size; ++i) sum += pow(gpu_out[i] - cpu_out[i], 2);
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for(i = 0; i < del_size; ++i) {
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//printf("%f %f\n", cpu_del[i], gpu_del[i]);
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del_sum += pow(cpu_del[i] - gpu_del[i], 2);
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float sum = 0;
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float del_sum = 0;
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for(i = 0; i < size; ++i) sum += pow(gpu_out[i] - cpu_out[i], 2);
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for(i = 0; i < del_size; ++i) {
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//printf("%f %f\n", cpu_del[i], gpu_del[i]);
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del_sum += pow(cpu_del[i] - gpu_del[i], 2);
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}
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printf("GPU cost: %f, CPU cost: %f\n", gpu_cost, cpu_cost);
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printf("gpu: %f sec, cpu: %f sec, diff: %f, delta diff: %f, size: %d\n", gpu_sec, cpu_sec, sum, del_sum, size);
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*/
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*/
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}
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void test_col2im()
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{
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float col[] = {1,2,1,2,
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1,2,1,2,
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1,2,1,2,
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1,2,1,2,
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1,2,1,2,
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1,2,1,2,
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1,2,1,2,
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1,2,1,2,
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1,2,1,2};
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1,2,1,2,
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1,2,1,2,
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1,2,1,2,
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1,2,1,2,
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1,2,1,2,
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1,2,1,2,
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1,2,1,2,
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1,2,1,2};
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float im[16] = {0};
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int batch = 1;
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int channels = 1;
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@ -117,289 +117,304 @@ void test_col2im()
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int stride = 1;
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int pad = 0;
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col2im_gpu(col, batch,
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channels, height, width,
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ksize, stride, pad, im);
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channels, height, width,
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ksize, stride, pad, im);
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int i;
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for(i = 0; i < 16; ++i)printf("%f,", im[i]);
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printf("\n");
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/*
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float data_im[] = {
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1,2,3,4,
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5,6,7,8,
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9,10,11,12
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};
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float data_col[18] = {0};
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im2col_cpu(data_im, batch,
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channels, height, width,
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ksize, stride, pad, data_col) ;
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for(i = 0; i < 18; ++i)printf("%f,", data_col[i]);
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printf("\n");
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*/
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float data_im[] = {
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1,2,3,4,
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5,6,7,8,
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9,10,11,12
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};
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float data_col[18] = {0};
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im2col_cpu(data_im, batch,
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channels, height, width,
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ksize, stride, pad, data_col) ;
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for(i = 0; i < 18; ++i)printf("%f,", data_col[i]);
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printf("\n");
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*/
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}
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#endif
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void test_convolve_matrix()
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{
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image dog = load_image("dog.jpg",300,400);
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printf("dog channels %d\n", dog.c);
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image dog = load_image("dog.jpg",300,400);
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printf("dog channels %d\n", dog.c);
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int size = 11;
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int stride = 4;
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int n = 40;
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float *filters = make_random_image(size, size, dog.c*n).data;
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int size = 11;
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int stride = 4;
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int n = 40;
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float *filters = make_random_image(size, size, dog.c*n).data;
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int mw = ((dog.h-size)/stride+1)*((dog.w-size)/stride+1);
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int mh = (size*size*dog.c);
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float *matrix = calloc(mh*mw, sizeof(float));
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int mw = ((dog.h-size)/stride+1)*((dog.w-size)/stride+1);
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int mh = (size*size*dog.c);
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float *matrix = calloc(mh*mw, sizeof(float));
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image edge = make_image((dog.h-size)/stride+1, (dog.w-size)/stride+1, n);
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image edge = make_image((dog.h-size)/stride+1, (dog.w-size)/stride+1, n);
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int i;
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clock_t start = clock(), end;
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for(i = 0; i < 1000; ++i){
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im2col_cpu(dog.data,1, dog.c, dog.h, dog.w, size, stride, 0, matrix);
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gemm(0,0,n,mw,mh,1,filters,mh,matrix,mw,1,edge.data,mw);
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}
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end = clock();
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printf("Convolutions: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
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show_image_layers(edge, "Test Convolve");
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cvWaitKey(0);
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int i;
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clock_t start = clock(), end;
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for(i = 0; i < 1000; ++i){
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im2col_cpu(dog.data,1, dog.c, dog.h, dog.w, size, stride, 0, matrix);
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gemm(0,0,n,mw,mh,1,filters,mh,matrix,mw,1,edge.data,mw);
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}
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end = clock();
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printf("Convolutions: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
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show_image_layers(edge, "Test Convolve");
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cvWaitKey(0);
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}
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void test_color()
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{
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image dog = load_image("test_color.png", 300, 400);
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show_image_layers(dog, "Test Color");
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image dog = load_image("test_color.png", 300, 400);
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show_image_layers(dog, "Test Color");
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}
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void verify_convolutional_layer()
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{
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srand(0);
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int i;
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int n = 1;
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int stride = 1;
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int size = 3;
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float eps = .00000001;
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image test = make_random_image(5,5, 1);
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convolutional_layer layer = *make_convolutional_layer(1,test.h,test.w,test.c, n, size, stride, 0, RELU,0,0,0);
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image out = get_convolutional_image(layer);
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float **jacobian = calloc(test.h*test.w*test.c, sizeof(float));
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srand(0);
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int i;
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int n = 1;
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int stride = 1;
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int size = 3;
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float eps = .00000001;
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image test = make_random_image(5,5, 1);
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convolutional_layer layer = *make_convolutional_layer(1,test.h,test.w,test.c, n, size, stride, 0, RELU,0,0,0);
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image out = get_convolutional_image(layer);
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float **jacobian = calloc(test.h*test.w*test.c, sizeof(float));
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forward_convolutional_layer(layer, test.data);
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image base = copy_image(out);
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forward_convolutional_layer(layer, test.data);
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image base = copy_image(out);
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for(i = 0; i < test.h*test.w*test.c; ++i){
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test.data[i] += eps;
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forward_convolutional_layer(layer, test.data);
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image partial = copy_image(out);
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subtract_image(partial, base);
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scale_image(partial, 1/eps);
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jacobian[i] = partial.data;
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test.data[i] -= eps;
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}
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float **jacobian2 = calloc(out.h*out.w*out.c, sizeof(float));
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image in_delta = make_image(test.h, test.w, test.c);
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image out_delta = get_convolutional_delta(layer);
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for(i = 0; i < out.h*out.w*out.c; ++i){
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out_delta.data[i] = 1;
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backward_convolutional_layer(layer, in_delta.data);
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image partial = copy_image(in_delta);
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jacobian2[i] = partial.data;
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out_delta.data[i] = 0;
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}
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int j;
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float *j1 = calloc(test.h*test.w*test.c*out.h*out.w*out.c, sizeof(float));
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float *j2 = calloc(test.h*test.w*test.c*out.h*out.w*out.c, sizeof(float));
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for(i = 0; i < test.h*test.w*test.c; ++i){
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for(j =0 ; j < out.h*out.w*out.c; ++j){
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j1[i*out.h*out.w*out.c + j] = jacobian[i][j];
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j2[i*out.h*out.w*out.c + j] = jacobian2[j][i];
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printf("%f %f\n", jacobian[i][j], jacobian2[j][i]);
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}
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}
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for(i = 0; i < test.h*test.w*test.c; ++i){
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test.data[i] += eps;
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forward_convolutional_layer(layer, test.data);
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image partial = copy_image(out);
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subtract_image(partial, base);
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scale_image(partial, 1/eps);
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jacobian[i] = partial.data;
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test.data[i] -= eps;
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}
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float **jacobian2 = calloc(out.h*out.w*out.c, sizeof(float));
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image in_delta = make_image(test.h, test.w, test.c);
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image out_delta = get_convolutional_delta(layer);
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for(i = 0; i < out.h*out.w*out.c; ++i){
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out_delta.data[i] = 1;
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backward_convolutional_layer(layer, in_delta.data);
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image partial = copy_image(in_delta);
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jacobian2[i] = partial.data;
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out_delta.data[i] = 0;
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}
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int j;
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float *j1 = calloc(test.h*test.w*test.c*out.h*out.w*out.c, sizeof(float));
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float *j2 = calloc(test.h*test.w*test.c*out.h*out.w*out.c, sizeof(float));
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for(i = 0; i < test.h*test.w*test.c; ++i){
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for(j =0 ; j < out.h*out.w*out.c; ++j){
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j1[i*out.h*out.w*out.c + j] = jacobian[i][j];
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j2[i*out.h*out.w*out.c + j] = jacobian2[j][i];
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printf("%f %f\n", jacobian[i][j], jacobian2[j][i]);
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}
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}
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image mj1 = float_to_image(test.w*test.h*test.c, out.w*out.h*out.c, 1, j1);
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image mj2 = float_to_image(test.w*test.h*test.c, out.w*out.h*out.c, 1, j2);
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printf("%f %f\n", avg_image_layer(mj1,0), avg_image_layer(mj2,0));
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show_image(mj1, "forward jacobian");
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show_image(mj2, "backward jacobian");
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image mj1 = float_to_image(test.w*test.h*test.c, out.w*out.h*out.c, 1, j1);
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image mj2 = float_to_image(test.w*test.h*test.c, out.w*out.h*out.c, 1, j2);
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printf("%f %f\n", avg_image_layer(mj1,0), avg_image_layer(mj2,0));
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show_image(mj1, "forward jacobian");
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show_image(mj2, "backward jacobian");
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}
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void test_load()
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{
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image dog = load_image("dog.jpg", 300, 400);
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show_image(dog, "Test Load");
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show_image_layers(dog, "Test Load");
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image dog = load_image("dog.jpg", 300, 400);
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show_image(dog, "Test Load");
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show_image_layers(dog, "Test Load");
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}
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void test_upsample()
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{
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image dog = load_image("dog.jpg", 300, 400);
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int n = 3;
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image up = make_image(n*dog.h, n*dog.w, dog.c);
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upsample_image(dog, n, up);
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show_image(up, "Test Upsample");
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show_image_layers(up, "Test Upsample");
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image dog = load_image("dog.jpg", 300, 400);
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int n = 3;
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image up = make_image(n*dog.h, n*dog.w, dog.c);
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upsample_image(dog, n, up);
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show_image(up, "Test Upsample");
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show_image_layers(up, "Test Upsample");
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}
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void test_rotate()
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{
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int i;
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image dog = load_image("dog.jpg",300,400);
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clock_t start = clock(), end;
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for(i = 0; i < 1001; ++i){
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rotate_image(dog);
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}
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end = clock();
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printf("Rotations: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
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show_image(dog, "Test Rotate");
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int i;
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image dog = load_image("dog.jpg",300,400);
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clock_t start = clock(), end;
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for(i = 0; i < 1001; ++i){
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rotate_image(dog);
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}
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end = clock();
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printf("Rotations: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
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show_image(dog, "Test Rotate");
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image random = make_random_image(3,3,3);
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show_image(random, "Test Rotate Random");
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rotate_image(random);
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show_image(random, "Test Rotate Random");
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rotate_image(random);
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show_image(random, "Test Rotate Random");
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image random = make_random_image(3,3,3);
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show_image(random, "Test Rotate Random");
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rotate_image(random);
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show_image(random, "Test Rotate Random");
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rotate_image(random);
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show_image(random, "Test Rotate Random");
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}
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void test_parser()
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{
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network net = parse_network_cfg("cfg/trained_imagenet.cfg");
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network net = parse_network_cfg("cfg/trained_imagenet.cfg");
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save_network(net, "cfg/trained_imagenet_smaller.cfg");
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}
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void test_data()
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||||
{
|
||||
char *labels[] = {"cat","dog"};
|
||||
data train = load_data_image_pathfile_random("train_paths.txt", 101,labels, 2, 300, 400);
|
||||
free_data(train);
|
||||
}
|
||||
|
||||
void train_asirra()
|
||||
{
|
||||
network net = parse_network_cfg("cfg/imagenet.cfg");
|
||||
network net = parse_network_cfg("cfg/imagenet.cfg");
|
||||
int imgs = 1000/net.batch+1;
|
||||
//imgs = 1;
|
||||
srand(2222222);
|
||||
int i = 0;
|
||||
char *labels[] = {"cat","dog"};
|
||||
clock_t time;
|
||||
while(1){
|
||||
i += 1;
|
||||
time=clock();
|
||||
data train = load_data_image_pathfile_random("data/assira/train.list", imgs*net.batch, labels, 2, 256, 256);
|
||||
normalize_data_rows(train);
|
||||
printf("Loaded: %lf seconds\n", sec(clock()-time));
|
||||
time=clock();
|
||||
//float loss = train_network_data(net, train, imgs);
|
||||
float loss = 0;
|
||||
printf("%d: %f, Time: %lf seconds\n", i*net.batch*imgs, loss, sec(clock()-time));
|
||||
free_data(train);
|
||||
if(i%10==0){
|
||||
char buff[256];
|
||||
sprintf(buff, "cfg/asirra_backup_%d.cfg", i);
|
||||
save_network(net, buff);
|
||||
}
|
||||
//lr *= .99;
|
||||
}
|
||||
}
|
||||
srand(2222222);
|
||||
int i = 0;
|
||||
char *labels[] = {"cat","dog"};
|
||||
|
||||
void train_imagenet()
|
||||
{
|
||||
float avg_loss = 1;
|
||||
//network net = parse_network_cfg("/home/pjreddie/imagenet_backup/alexnet_1270.cfg");
|
||||
network net = parse_network_cfg("cfg/imagenet.cfg");
|
||||
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
|
||||
int imgs = 1000/net.batch+1;
|
||||
srand(time(0));
|
||||
int i = 0;
|
||||
char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list");
|
||||
list *plist = get_paths("/data/imagenet/cls.train.list");
|
||||
list *plist = get_paths("data/assira/train.list");
|
||||
char **paths = (char **)list_to_array(plist);
|
||||
printf("%d\n", plist->size);
|
||||
int m = plist->size;
|
||||
free_list(plist);
|
||||
|
||||
clock_t time;
|
||||
while(1){
|
||||
i += 1;
|
||||
|
||||
while(1){
|
||||
i += 1;
|
||||
time=clock();
|
||||
data train = load_data_random(imgs*net.batch, paths, plist->size, labels, 1000, 256, 256);
|
||||
//translate_data_rows(train, -144);
|
||||
data train = load_data_random(imgs*net.batch, paths, m, labels, 2, 256, 256);
|
||||
normalize_data_rows(train);
|
||||
printf("Loaded: %lf seconds\n", sec(clock()-time));
|
||||
time=clock();
|
||||
#ifdef GPU
|
||||
float loss = train_network_data_gpu(net, train, imgs);
|
||||
avg_loss = avg_loss*.9 + loss*.1;
|
||||
printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), i*imgs*net.batch);
|
||||
#endif
|
||||
free_data(train);
|
||||
if(i%10==0){
|
||||
char buff[256];
|
||||
sprintf(buff, "/home/pjreddie/imagenet_backup/imagenet_%d.cfg", i);
|
||||
save_network(net, buff);
|
||||
}
|
||||
}
|
||||
//float loss = train_network_data(net, train, imgs);
|
||||
float loss = 0;
|
||||
printf("%d: %f, Time: %lf seconds\n", i*net.batch*imgs, loss, sec(clock()-time));
|
||||
free_data(train);
|
||||
if(i%10==0){
|
||||
char buff[256];
|
||||
sprintf(buff, "cfg/asirra_backup_%d.cfg", i);
|
||||
save_network(net, buff);
|
||||
}
|
||||
//lr *= .99;
|
||||
}
|
||||
}
|
||||
|
||||
void validate_imagenet(char *filename)
|
||||
void train_detection_net()
|
||||
{
|
||||
int i;
|
||||
network net = parse_network_cfg(filename);
|
||||
srand(time(0));
|
||||
|
||||
char **labels = get_labels("/home/pjreddie/data/imagenet/cls.val.labels.list");
|
||||
char *path = "/home/pjreddie/data/imagenet/cls.val.list";
|
||||
|
||||
clock_t time;
|
||||
float avg_acc = 0;
|
||||
int splits = 50;
|
||||
for(i = 0; i < splits; ++i){
|
||||
time=clock();
|
||||
data val = load_data_image_pathfile_part(path, i, splits, labels, 1000, 256, 256);
|
||||
normalize_data_rows(val);
|
||||
printf("Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock()-time));
|
||||
time=clock();
|
||||
#ifdef GPU
|
||||
float acc = network_accuracy_gpu(net, val);
|
||||
avg_acc += acc;
|
||||
printf("%d: %f, %f avg, %lf seconds, %d images\n", i, acc, avg_acc/(i+1), sec(clock()-time), val.X.rows);
|
||||
#endif
|
||||
free_data(val);
|
||||
}
|
||||
}
|
||||
|
||||
void train_imagenet_small()
|
||||
{
|
||||
network net = parse_network_cfg("cfg/imagenet_small.cfg");
|
||||
float avg_loss = 1;
|
||||
//network net = parse_network_cfg("/home/pjreddie/imagenet_backup/alexnet_1270.cfg");
|
||||
network net = parse_network_cfg("cfg/detnet.cfg");
|
||||
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
|
||||
int imgs=1;
|
||||
srand(111222);
|
||||
int imgs = 1000/net.batch+1;
|
||||
srand(time(0));
|
||||
int i = 0;
|
||||
char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list");
|
||||
list *plist = get_paths("/data/imagenet/cls.train.list");
|
||||
char **paths = (char **)list_to_array(plist);
|
||||
printf("%d\n", plist->size);
|
||||
clock_t time;
|
||||
|
||||
i += 1;
|
||||
time=clock();
|
||||
data train = load_data_random(imgs*net.batch, paths, plist->size, labels, 1000, 256, 256);
|
||||
normalize_data_rows(train);
|
||||
printf("Loaded: %lf seconds\n", sec(clock()-time));
|
||||
time=clock();
|
||||
while(1){
|
||||
i += 1;
|
||||
time=clock();
|
||||
data train = load_data_random(imgs*net.batch, paths, plist->size, labels, 1000, 256, 256);
|
||||
//translate_data_rows(train, -144);
|
||||
normalize_data_rows(train);
|
||||
printf("Loaded: %lf seconds\n", sec(clock()-time));
|
||||
time=clock();
|
||||
#ifdef GPU
|
||||
float loss = train_network_data_gpu(net, train, imgs);
|
||||
printf("%d: %f, %lf seconds, %d images\n", i, loss, sec(clock()-time), i*imgs*net.batch);
|
||||
float loss = train_network_data_gpu(net, train, imgs);
|
||||
avg_loss = avg_loss*.9 + loss*.1;
|
||||
printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), i*imgs*net.batch);
|
||||
#endif
|
||||
free_data(train);
|
||||
char buff[256];
|
||||
sprintf(buff, "/home/pjreddie/imagenet_backup/imagenet_backup_slower_larger_%d.cfg", i);
|
||||
save_network(net, buff);
|
||||
free_data(train);
|
||||
if(i%10==0){
|
||||
char buff[256];
|
||||
sprintf(buff, "/home/pjreddie/imagenet_backup/imagenet_%d.cfg", i);
|
||||
save_network(net, buff);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
void train_imagenet()
|
||||
{
|
||||
float avg_loss = 1;
|
||||
//network net = parse_network_cfg("/home/pjreddie/imagenet_backup/alexnet_1270.cfg");
|
||||
network net = parse_network_cfg("cfg/alexnet.cfg");
|
||||
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
|
||||
int imgs = 1000/net.batch+1;
|
||||
srand(time(0));
|
||||
int i = 0;
|
||||
char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list");
|
||||
list *plist = get_paths("/data/imagenet/cls.train.list");
|
||||
char **paths = (char **)list_to_array(plist);
|
||||
printf("%d\n", plist->size);
|
||||
clock_t time;
|
||||
while(1){
|
||||
i += 1;
|
||||
time=clock();
|
||||
data train = load_data_random(imgs*net.batch, paths, plist->size, labels, 1000, 256, 256);
|
||||
//translate_data_rows(train, -144);
|
||||
normalize_data_rows(train);
|
||||
printf("Loaded: %lf seconds\n", sec(clock()-time));
|
||||
time=clock();
|
||||
#ifdef GPU
|
||||
float loss = train_network_data_gpu(net, train, imgs);
|
||||
avg_loss = avg_loss*.9 + loss*.1;
|
||||
printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), i*imgs*net.batch);
|
||||
#endif
|
||||
free_data(train);
|
||||
if(i%10==0){
|
||||
char buff[256];
|
||||
sprintf(buff, "/home/pjreddie/imagenet_backup/imagenet_%d.cfg", i);
|
||||
save_network(net, buff);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void validate_imagenet(char *filename)
|
||||
{
|
||||
int i;
|
||||
network net = parse_network_cfg(filename);
|
||||
srand(time(0));
|
||||
|
||||
char **labels = get_labels("/home/pjreddie/data/imagenet/cls.val.labels.list");
|
||||
|
||||
list *plist = get_paths("/home/pjreddie/data/imagenet/cls.val.list");
|
||||
char **paths = (char **)list_to_array(plist);
|
||||
int m = plist->size;
|
||||
free_list(plist);
|
||||
|
||||
clock_t time;
|
||||
float avg_acc = 0;
|
||||
int splits = 50;
|
||||
|
||||
for(i = 0; i < splits; ++i){
|
||||
time=clock();
|
||||
char **part = paths+(i*m/splits);
|
||||
int num = (i+1)*m/splits - i*m/splits;
|
||||
data val = load_data(part, num, labels, 1000, 256, 256);
|
||||
normalize_data_rows(val);
|
||||
printf("Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock()-time));
|
||||
time=clock();
|
||||
#ifdef GPU
|
||||
float acc = network_accuracy_gpu(net, val);
|
||||
avg_acc += acc;
|
||||
printf("%d: %f, %f avg, %lf seconds, %d images\n", i, acc, avg_acc/(i+1), sec(clock()-time), val.X.rows);
|
||||
#endif
|
||||
free_data(val);
|
||||
}
|
||||
}
|
||||
|
||||
void test_imagenet()
|
||||
{
|
||||
network net = parse_network_cfg("cfg/imagenet_test.cfg");
|
||||
network net = parse_network_cfg("cfg/imagenet_test.cfg");
|
||||
//imgs=1;
|
||||
srand(2222222);
|
||||
int i = 0;
|
||||
@ -431,32 +446,6 @@ void test_visualize(char *filename)
|
||||
visualize_network(net);
|
||||
cvWaitKey(0);
|
||||
}
|
||||
void test_full()
|
||||
{
|
||||
network net = parse_network_cfg("cfg/backup_1300.cfg");
|
||||
srand(2222222);
|
||||
int i,j;
|
||||
int total = 100;
|
||||
char *labels[] = {"cat","dog"};
|
||||
FILE *fp = fopen("preds.txt","w");
|
||||
for(i = 0; i < total; ++i){
|
||||
visualize_network(net);
|
||||
cvWaitKey(100);
|
||||
data test = load_data_image_pathfile_part("data/assira/test.list", i, total, labels, 2, 256, 256);
|
||||
image im = float_to_image(256, 256, 3,test.X.vals[0]);
|
||||
show_image(im, "input");
|
||||
cvWaitKey(100);
|
||||
normalize_data_rows(test);
|
||||
for(j = 0; j < test.X.rows; ++j){
|
||||
float *x = test.X.vals[j];
|
||||
forward_network(net, x, 0, 0);
|
||||
int class = get_predicted_class_network(net);
|
||||
fprintf(fp, "%d\n", class);
|
||||
}
|
||||
free_data(test);
|
||||
}
|
||||
fclose(fp);
|
||||
}
|
||||
|
||||
void test_cifar10()
|
||||
{
|
||||
@ -675,88 +664,74 @@ void flip_network()
|
||||
save_network(net, "cfg/voc_imagenet_rev.cfg");
|
||||
}
|
||||
|
||||
void tune_VOC()
|
||||
|
||||
void visualize_cat()
|
||||
{
|
||||
network net = parse_network_cfg("cfg/voc_start.cfg");
|
||||
srand(2222222);
|
||||
int i = 20;
|
||||
char *labels[] = {"aeroplane","bicycle","bird","boat","bottle","bus","car","cat","chair","cow","diningtable","dog","horse","motorbike","person","pottedplant","sheep","sofa","train","tvmonitor"};
|
||||
float lr = .000005;
|
||||
float momentum = .9;
|
||||
float decay = 0.0001;
|
||||
while(i++ < 1000 || 1){
|
||||
data train = load_data_image_pathfile_random("/home/pjreddie/VOC2012/trainval_paths.txt", 10, labels, 20, 256, 256);
|
||||
|
||||
image im = float_to_image(256, 256, 3,train.X.vals[0]);
|
||||
show_image(im, "input");
|
||||
visualize_network(net);
|
||||
cvWaitKey(100);
|
||||
|
||||
translate_data_rows(train, -144);
|
||||
clock_t start = clock(), end;
|
||||
float loss = train_network_sgd(net, train, 10);
|
||||
end = clock();
|
||||
printf("%d: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", i, loss, (float)(end-start)/CLOCKS_PER_SEC, lr, momentum, decay);
|
||||
free_data(train);
|
||||
/*
|
||||
if(i%10==0){
|
||||
char buff[256];
|
||||
sprintf(buff, "/home/pjreddie/voc_cfg/voc_ramp_%d.cfg", i);
|
||||
save_network(net, buff);
|
||||
}
|
||||
*/
|
||||
//lr *= .99;
|
||||
}
|
||||
}
|
||||
|
||||
int voc_size(int x)
|
||||
{
|
||||
x = x-1+3;
|
||||
x = x-1+3;
|
||||
x = x-1+3;
|
||||
x = (x-1)*2+1;
|
||||
x = x-1+5;
|
||||
x = (x-1)*2+1;
|
||||
x = (x-1)*4+11;
|
||||
return x;
|
||||
}
|
||||
|
||||
image features_output_size(network net, IplImage *src, int outh, int outw)
|
||||
{
|
||||
int h = voc_size(outh);
|
||||
int w = voc_size(outw);
|
||||
fprintf(stderr, "%d %d\n", h, w);
|
||||
|
||||
IplImage *sized = cvCreateImage(cvSize(w,h), src->depth, src->nChannels);
|
||||
cvResize(src, sized, CV_INTER_LINEAR);
|
||||
image im = ipl_to_image(sized);
|
||||
//normalize_array(im.data, im.h*im.w*im.c);
|
||||
translate_image(im, -144);
|
||||
network net = parse_network_cfg("cfg/voc_imagenet.cfg");
|
||||
image im = load_image("data/cat.png", 0, 0);
|
||||
printf("Processing %dx%d image\n", im.h, im.w);
|
||||
resize_network(net, im.h, im.w, im.c);
|
||||
forward_network(net, im.data, 0, 0);
|
||||
image out = get_network_image(net);
|
||||
free_image(im);
|
||||
cvReleaseImage(&sized);
|
||||
return copy_image(out);
|
||||
|
||||
visualize_network(net);
|
||||
cvWaitKey(0);
|
||||
}
|
||||
|
||||
void features_VOC_image_size(char *image_path, int h, int w)
|
||||
|
||||
void test_gpu_net()
|
||||
{
|
||||
int j;
|
||||
network net = parse_network_cfg("cfg/voc_imagenet.cfg");
|
||||
fprintf(stderr, "%s\n", image_path);
|
||||
|
||||
IplImage* src = 0;
|
||||
if( (src = cvLoadImage(image_path,-1)) == 0 ) file_error(image_path);
|
||||
image out = features_output_size(net, src, h, w);
|
||||
for(j = 0; j < out.c*out.h*out.w; ++j){
|
||||
if(j != 0) printf(",");
|
||||
printf("%g", out.data[j]);
|
||||
srand(222222);
|
||||
network net = parse_network_cfg("cfg/nist.cfg");
|
||||
data train = load_categorical_data_csv("data/mnist/mnist_train.csv", 0, 10);
|
||||
data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10);
|
||||
translate_data_rows(train, -144);
|
||||
translate_data_rows(test, -144);
|
||||
int count = 0;
|
||||
int iters = 1000/net.batch;
|
||||
while(++count <= 5){
|
||||
clock_t start = clock(), end;
|
||||
float loss = train_network_sgd(net, train, iters);
|
||||
end = clock();
|
||||
float test_acc = network_accuracy(net, test);
|
||||
printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC, net.learning_rate, net.momentum, net.decay);
|
||||
}
|
||||
printf("\n");
|
||||
free_image(out);
|
||||
cvReleaseImage(&src);
|
||||
#ifdef GPU
|
||||
count = 0;
|
||||
srand(222222);
|
||||
net = parse_network_cfg("cfg/nist.cfg");
|
||||
while(++count <= 5){
|
||||
clock_t start = clock(), end;
|
||||
float loss = train_network_sgd_gpu(net, train, iters);
|
||||
end = clock();
|
||||
float test_acc = network_accuracy(net, test);
|
||||
printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC, net.learning_rate, net.momentum, net.decay);
|
||||
}
|
||||
#endif
|
||||
}
|
||||
|
||||
|
||||
int main(int argc, char *argv[])
|
||||
{
|
||||
if(argc < 2){
|
||||
fprintf(stderr, "usage: %s <function>\n", argv[0]);
|
||||
return 0;
|
||||
}
|
||||
if(0==strcmp(argv[1], "train")) train_imagenet();
|
||||
else if(0==strcmp(argv[1], "asirra")) train_asirra();
|
||||
else if(0==strcmp(argv[1], "nist")) train_nist();
|
||||
else if(0==strcmp(argv[1], "test_correct")) test_gpu_net();
|
||||
else if(0==strcmp(argv[1], "test")) test_imagenet();
|
||||
else if(0==strcmp(argv[1], "visualize")) test_visualize(argv[2]);
|
||||
else if(0==strcmp(argv[1], "valid")) validate_imagenet(argv[2]);
|
||||
#ifdef GPU
|
||||
else if(0==strcmp(argv[1], "test_gpu")) test_gpu_blas();
|
||||
#endif
|
||||
test_parser();
|
||||
fprintf(stderr, "Success!\n");
|
||||
return 0;
|
||||
}
|
||||
|
||||
/*
|
||||
void visualize_imagenet_topk(char *filename)
|
||||
{
|
||||
int i,j,k,l;
|
||||
@ -873,19 +848,6 @@ void visualize_imagenet_features(char *filename)
|
||||
}
|
||||
cvWaitKey(0);
|
||||
}
|
||||
|
||||
void visualize_cat()
|
||||
{
|
||||
network net = parse_network_cfg("cfg/voc_imagenet.cfg");
|
||||
image im = load_image("data/cat.png", 0, 0);
|
||||
printf("Processing %dx%d image\n", im.h, im.w);
|
||||
resize_network(net, im.h, im.w, im.c);
|
||||
forward_network(net, im.data, 0, 0);
|
||||
|
||||
visualize_network(net);
|
||||
cvWaitKey(0);
|
||||
}
|
||||
|
||||
void features_VOC_image(char *image_file, char *image_dir, char *out_dir, int flip, int interval)
|
||||
{
|
||||
int i,j;
|
||||
@ -992,57 +954,4 @@ void test_distribution()
|
||||
cvWaitKey(0);
|
||||
cvWaitKey(0);
|
||||
}
|
||||
|
||||
void test_gpu_net()
|
||||
{
|
||||
srand(222222);
|
||||
network net = parse_network_cfg("cfg/nist.cfg");
|
||||
data train = load_categorical_data_csv("data/mnist/mnist_train.csv", 0, 10);
|
||||
data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10);
|
||||
translate_data_rows(train, -144);
|
||||
translate_data_rows(test, -144);
|
||||
int count = 0;
|
||||
int iters = 1000/net.batch;
|
||||
while(++count <= 5){
|
||||
clock_t start = clock(), end;
|
||||
float loss = train_network_sgd(net, train, iters);
|
||||
end = clock();
|
||||
float test_acc = network_accuracy(net, test);
|
||||
printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC, net.learning_rate, net.momentum, net.decay);
|
||||
}
|
||||
#ifdef GPU
|
||||
count = 0;
|
||||
srand(222222);
|
||||
net = parse_network_cfg("cfg/nist.cfg");
|
||||
while(++count <= 5){
|
||||
clock_t start = clock(), end;
|
||||
float loss = train_network_sgd_gpu(net, train, iters);
|
||||
end = clock();
|
||||
float test_acc = network_accuracy(net, test);
|
||||
printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC, net.learning_rate, net.momentum, net.decay);
|
||||
}
|
||||
#endif
|
||||
}
|
||||
|
||||
|
||||
int main(int argc, char *argv[])
|
||||
{
|
||||
if(argc < 2){
|
||||
fprintf(stderr, "usage: %s <function>\n", argv[0]);
|
||||
return 0;
|
||||
}
|
||||
if(0==strcmp(argv[1], "train")) train_imagenet();
|
||||
else if(0==strcmp(argv[1], "asirra")) train_asirra();
|
||||
else if(0==strcmp(argv[1], "nist")) train_nist();
|
||||
else if(0==strcmp(argv[1], "train_small")) train_imagenet_small();
|
||||
else if(0==strcmp(argv[1], "test_correct")) test_gpu_net();
|
||||
else if(0==strcmp(argv[1], "test")) test_imagenet();
|
||||
else if(0==strcmp(argv[1], "visualize")) test_visualize(argv[2]);
|
||||
else if(0==strcmp(argv[1], "valid")) validate_imagenet(argv[2]);
|
||||
#ifdef GPU
|
||||
else if(0==strcmp(argv[1], "test_gpu")) test_gpu_blas();
|
||||
#endif
|
||||
test_parser();
|
||||
fprintf(stderr, "Success!\n");
|
||||
return 0;
|
||||
}
|
||||
*/
|
||||
|
||||
120
src/data.c
120
src/data.c
@ -19,10 +19,28 @@ list *get_paths(char *filename)
|
||||
return lines;
|
||||
}
|
||||
|
||||
void fill_truth_det(char *path, float *truth)
|
||||
void fill_truth_detection(char *path, float *truth, int height, int width, int num_height, int num_width, float scale)
|
||||
{
|
||||
find_replace(path, "imgs", "det");
|
||||
find_replace(path, ".JPEG", ".txt");
|
||||
int box_height = height/num_height;
|
||||
int box_width = width/num_width;
|
||||
char *labelpath = find_replace(path, "imgs", "det");
|
||||
labelpath = find_replace(labelpath, ".JPEG", ".txt");
|
||||
FILE *file = fopen(labelpath, "r");
|
||||
int x, y, h, w;
|
||||
while(fscanf(file, "%d %d %d %d", &x, &y, &w, &h) == 4){
|
||||
int i = x/box_width;
|
||||
int j = y/box_height;
|
||||
float dh = (float)(x%box_width)/box_height;
|
||||
float dw = (float)(y%box_width)/box_width;
|
||||
float sh = h/scale;
|
||||
float sw = w/scale;
|
||||
int index = (i+j*num_width)*5;
|
||||
truth[index++] = 1;
|
||||
truth[index++] = dh;
|
||||
truth[index++] = dw;
|
||||
truth[index++] = sh;
|
||||
truth[index++] = sw;
|
||||
}
|
||||
}
|
||||
|
||||
void fill_truth(char *path, char **labels, int k, float *truth)
|
||||
@ -36,32 +54,52 @@ void fill_truth(char *path, char **labels, int k, float *truth)
|
||||
}
|
||||
}
|
||||
|
||||
data load_data_image_paths(char **paths, int n, char **labels, int k, int h, int w)
|
||||
matrix load_image_paths(char **paths, int n, int h, int w)
|
||||
{
|
||||
int i;
|
||||
data d;
|
||||
d.shallow = 0;
|
||||
d.X.rows = n;
|
||||
d.X.vals = calloc(d.X.rows, sizeof(float*));
|
||||
d.X.cols = 0;
|
||||
d.y = make_matrix(n, k);
|
||||
matrix X;
|
||||
X.rows = n;
|
||||
X.vals = calloc(X.rows, sizeof(float*));
|
||||
X.cols = 0;
|
||||
|
||||
for(i = 0; i < n; ++i){
|
||||
image im = load_image_color(paths[i], h, w);
|
||||
d.X.vals[i] = im.data;
|
||||
d.X.cols = im.h*im.w*im.c;
|
||||
X.vals[i] = im.data;
|
||||
X.cols = im.h*im.w*im.c;
|
||||
}
|
||||
return X;
|
||||
}
|
||||
|
||||
matrix load_labels_paths(char **paths, int n, char **labels, int k)
|
||||
{
|
||||
matrix y = make_matrix(n, k);
|
||||
int i;
|
||||
for(i = 0; i < n; ++i){
|
||||
fill_truth(paths[i], labels, k, d.y.vals[i]);
|
||||
fill_truth(paths[i], labels, k, y.vals[i]);
|
||||
}
|
||||
return d;
|
||||
return y;
|
||||
}
|
||||
|
||||
matrix load_labels_detection(char **paths, int n, int height, int width, int num_height, int num_width, float scale)
|
||||
{
|
||||
int k = num_height*num_width*5;
|
||||
matrix y = make_matrix(n, k);
|
||||
int i;
|
||||
for(i = 0; i < n; ++i){
|
||||
fill_truth_detection(paths[i], y.vals[i], height, width, num_height, num_width, scale);
|
||||
}
|
||||
return y;
|
||||
}
|
||||
|
||||
data load_data_image_pathfile(char *filename, char **labels, int k, int h, int w)
|
||||
{
|
||||
list *plist = get_paths(filename);
|
||||
char **paths = (char **)list_to_array(plist);
|
||||
data d = load_data_image_paths(paths, plist->size, labels, k, h, w);
|
||||
int n = plist->size;
|
||||
data d;
|
||||
d.shallow = 0;
|
||||
d.X = load_image_paths(paths, n, h, w);
|
||||
d.y = load_labels_paths(paths, n, labels, k);
|
||||
free_list_contents(plist);
|
||||
free_list(plist);
|
||||
free(paths);
|
||||
@ -87,16 +125,29 @@ void free_data(data d)
|
||||
}
|
||||
}
|
||||
|
||||
data load_data_image_pathfile_part(char *filename, int part, int total, char **labels, int k, int h, int w)
|
||||
data load_data_detection_random(int n, char **paths, int m, char **labels, int h, int w, int nh, int nw, float scale)
|
||||
{
|
||||
list *plist = get_paths(filename);
|
||||
char **paths = (char **)list_to_array(plist);
|
||||
int start = part*plist->size/total;
|
||||
int end = (part+1)*plist->size/total;
|
||||
data d = load_data_image_paths(paths+start, end-start, labels, k, h, w);
|
||||
free_list_contents(plist);
|
||||
free_list(plist);
|
||||
free(paths);
|
||||
char **random_paths = calloc(n, sizeof(char*));
|
||||
int i;
|
||||
for(i = 0; i < n; ++i){
|
||||
int index = rand()%m;
|
||||
random_paths[i] = paths[index];
|
||||
if(i == 0) printf("%s\n", paths[index]);
|
||||
}
|
||||
data d;
|
||||
d.shallow = 0;
|
||||
d.X = load_image_paths(random_paths, n, h, w);
|
||||
d.y = load_labels_detection(random_paths, n, h, w, nh, nw, scale);
|
||||
free(random_paths);
|
||||
return d;
|
||||
}
|
||||
|
||||
data load_data(char **paths, int n, char **labels, int k, int h, int w)
|
||||
{
|
||||
data d;
|
||||
d.shallow = 0;
|
||||
d.X = load_image_paths(paths, n, h, w);
|
||||
d.y = load_labels_paths(paths, n, labels, k);
|
||||
return d;
|
||||
}
|
||||
|
||||
@ -109,26 +160,7 @@ data load_data_random(int n, char **paths, int m, char **labels, int k, int h, i
|
||||
random_paths[i] = paths[index];
|
||||
if(i == 0) printf("%s\n", paths[index]);
|
||||
}
|
||||
data d = load_data_image_paths(random_paths, n, labels, k, h, w);
|
||||
free(random_paths);
|
||||
return d;
|
||||
}
|
||||
|
||||
data load_data_image_pathfile_random(char *filename, int n, char **labels, int k, int h, int w)
|
||||
{
|
||||
int i;
|
||||
list *plist = get_paths(filename);
|
||||
char **paths = (char **)list_to_array(plist);
|
||||
char **random_paths = calloc(n, sizeof(char*));
|
||||
for(i = 0; i < n; ++i){
|
||||
int index = rand()%plist->size;
|
||||
random_paths[i] = paths[index];
|
||||
if(i == 0) printf("%s\n", paths[index]);
|
||||
}
|
||||
data d = load_data_image_paths(random_paths, n, labels, k, h, w);
|
||||
free_list_contents(plist);
|
||||
free_list(plist);
|
||||
free(paths);
|
||||
data d = load_data(random_paths, n, labels, k, h, w);
|
||||
free(random_paths);
|
||||
return d;
|
||||
}
|
||||
|
||||
@ -12,12 +12,10 @@ typedef struct{
|
||||
|
||||
|
||||
void free_data(data d);
|
||||
data load_data(char **paths, int n, char **labels, int k, int h, int w);
|
||||
data load_data_random(int n, char **paths, int m, char **labels, int k, int h, int w);
|
||||
data load_data_detection_random(int n, char **paths, int m, char **labels, int h, int w, int nh, int nw, float scale);
|
||||
data load_data_image_pathfile(char *filename, char **labels, int k, int h, int w);
|
||||
data load_data_image_pathfile_part(char *filename, int part, int total,
|
||||
char **labels, int k, int h, int w);
|
||||
data load_data_image_pathfile_random(char *filename, int n, char **labels,
|
||||
int k, int h, int w);
|
||||
data load_cifar10_data(char *filename);
|
||||
data load_all_cifar10();
|
||||
list *get_paths(char *filename);
|
||||
|
||||
Loading…
Reference in New Issue
Block a user